
As a fundamental subject in mathematics and physics, the Laplacian is known and used, through its spectral study to extract relevant geometric properties from a manifold [1]} or a graph [2]} and plays a role in machine learning applications such as spectral clustering [3]}.

In continuous space, the eigenfunctions of the Laplace-Beltrami operator constitute the generalization of the Fourier basis on manifolds [1]}, [5]}.

In discrete space, the eigenvectors of the graph Laplacian form an orthonormal Fourier-type basis for functions on undirected graphs [6]}, [7]}, when undirected graphs are considered as a discretization or sampling of a manifold [8]}, [9]}, [10]}, [11]}. The eigenvalue associated with each eigenvector is related to the notion of frequency [6]}.

